Alternatives and similar repositories for uplift_modeling:

Users that are interested in uplift_modeling are comparing it to the libraries listed below

• CamDavidsonPilon / PyDataNY_2019_tutorial
  Repo for PyData 2019 Tutorial - New Trends in Estimation and Inference
  ☆25Updated 5 years ago
• saritmaitra / Segmentation-Clustering
  Recency, Frequency, and Monetary are three behavioral attributes and are quite simple, in that they can be easily computed for any databa…
  ☆15Updated last year
• hugopalmer / assortment_optimization
  Source code from my Master's thesis @Polytechnique Montréal. A solution to the assortment optimization problem, able to deal with large n…
  ☆18Updated 7 years ago
• vcerqueira / vest
  Automatic Feature Engineering for Time Series
  ☆17Updated 2 years ago
• drivendataorg / power-laws-cold-start
  ☆23Updated last year
• FlorianWilhelm / bhm-at-scale
  🪜 Bayesian Hierarchical Models at Scale
  ☆52Updated 3 years ago
• PGuti / Uplift
  About uplift modeling
  ☆30Updated 8 years ago
• sparsh-ai / reco-bandit
  Building recommender Systems using contextual bandit methods to address cold-start issue and online real-time learning
  ☆10Updated 3 years ago
• jgitr / opossum
  ☆16Updated 5 years ago
• MSRDL / TSA
  Time Series Anomaly Detection Toolkit
  ☆22Updated 6 years ago
• wwrechard / pydlm-lite
  The lite version of the package pydlm. A lite yet powerful Bayesian dynamic modeling library
  ☆12Updated 6 years ago
• brian-c-ogorman / ABanditTesting
  No Regrets: A deep dive comparison of bandits and A/B testing
  ☆47Updated 7 years ago
• alteryx / predict-household-poverty
  Predict the poverty of households in Costa Rica using automated feature engineering.
  ☆23Updated 4 years ago
• gdmarmerola / cfml_tools
  My collection of causal inference algorithms built on top of accessible, simple, out-of-the-box ML methods, aimed at being explainable an…
  ☆29Updated 2 years ago
• jehangiramjad / tslib
  Time Series Forecasting and Imputation
  ☆49Updated 3 years ago
• exploripy / exploripy
  Pre-Modelling Analysis of the data, by doing various exploratory data analysis and Statistical Test.
  ☆51Updated last year
• woobe / odsc_h2o_machine_learning
  Materials for Machine Learning with H2O Open Platform at ODSC Masterclass Summit 2017
  ☆12Updated 8 years ago
• alteryx / predict-correct-answer
  Predict whether a student will correctly answer a problem based on past performance using automated feature engineering
  ☆32Updated 4 years ago
• franck-durand / tsboost
  Time Series Forecasting Framework
  ☆41Updated 2 years ago
• larsroemheld / causalinf_ex_elasticity
  A full example for causal inference on real-world retail data, for elasticity estimation
  ☆50Updated 3 years ago
• gdmarmerola / random-stuff
  Random stuff I've been working on
  ☆28Updated last year
• andrewtavis / causeinfer
  Machine learning based causal inference/uplift in Python
  ☆59Updated last year
• Ibotta / mr_uplift
  Multiple Response Uplift (or heterogeneous treatment effects) package that builds and evaluates tradeoffs with multiple treatments and mu…
  ☆66Updated 7 months ago
• szilard / GBM-adv-workshop-Bp19
  Advanced GBM Workshop - Budapest, Nov 2019
  ☆12Updated 5 years ago
• AlxndrMlk / blogs-code
  Code for blog posts.
  ☆19Updated last year
• jamborta / tffm2
  ☆9Updated 3 years ago
• KishManani / m5-forecasting-tutorial
  An end-to-end tutorial to forecast the M5 dataset using feature engineering pipelines and gradient boosting.
  ☆17Updated 2 years ago
• rakutentech / spark-dirty-cat
  Similarity encoding of dirty categorical variables (strings)
  ☆20Updated 6 years ago
• kirrlix1994 / Retail_hero
  Reatail-hero X5 contest
  ☆13Updated 9 months ago
• yaronshap / GloballyConsistentRules
  Implementation of algorithms from the paper "Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application…
  ☆24Updated 2 years ago